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Gamblers' habit

机译：赌徒的习惯

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The theories used to understand and predict regular non-problem gambling are almost exclusively affective or cognitive-oriented. These include motives, self-esteem, image enhancement and illusions of control over random events. However, gambling is one of the most frequently purchased consumer products, and the frequency of past behavior has traditionally been viewed as "habit" by psychologists and marketers. While habit as the frequency of past behavior has been shown to be a strong predictor of future behavior in gambling, habit offers little additional insight into gambling behavior in that form. The frequency of past purchasing behavior is an important input to NBD-Dirichlet models that provide an enhanced ability to understand and predict future purchases of frequently purchased consumer package goods. NBD-Dirichlet models have been shown to provide an excellent fit to data for a broad range of frequently purchased goods and services for countries across the world. Applications of the NBD-Dirichlet models to data concerning gambling behavior show that these models consistently provide an even closer fit to the data than with other consumer models tested. The interpretation of NBD-Dirichlet output can provide more accurate benchmarks than cognitive or affective output to test changes to the gambling environment (e.g., more games, new games, warnings) and to gamblers (e.g., problem gambling). The implications and use of the NBD-Dirichlet statistics for gambling providers and public policy is discussed.
机译：用于理解和预测常规非问题赌博的理论几乎完全是情感或面向认知的。这些包括动机，自尊，图像增强和对随机事件的控制幻想。但是，赌博是最常购买的消费产品之一，心理学家和营销商通常将过去的行为频率视为“习惯”。虽然习惯性行为是过去赌博行为发生频率的强有力预测指标，但习惯性赌博对这种形式的赌博行为几乎没有任何额外的洞察力。过去购买行为的频率是NBD-Dirichlet模型的重要输入，NBD-Dirichlet模型提供了增强的能力，可以了解和预测经常购买的消费者包装商品的未来购买情况。 NBD-Dirichlet模型已被证明可以很好地拟合世界各国广泛购买的商品和服务的数据。 NBD-Dirichlet模型在与赌博行为有关的数据上的应用表明，与测试的其他消费者模型相比，这些模型始终如一地为数据提供更紧密的契合度。与认知或情感输出相比，对NBD-狄里克雷输出的解释可以提供更准确的基准，以测试对赌博环境（例如更多的游戏，新游戏，警告）和对赌徒（例如问题赌博）的变化。讨论了NBD-Dirichlet统计数据对赌博提供者和公共政策的影响和使用。

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